Computational modelling of the speed-accuracy tradeoff: No evidence for an association with depression symptomatology
←
→
Page content transcription
If your browser does not render page correctly, please read the page content below
Computational modelling of the speed–accuracy tradeoff: No
evidence for an association with depression symptomatology
James A. Grange1
1 School of Psychology, Keele University
Abstract
Background. Successful decision making often requires finding the right
balance between the speed and accuracy of responding: Emphasising speed
can lead to error-prone performance, yet emphasising accuracy leads to a
slowing of performance. Such speed–accuracy tradeoffs (SATs) therefore
require establishing appropriate response settings to optimise performance
in response to changing environmental demands. Such strategic adaptaion
of response settings relies on the striatal regions of human cortex, an area
implicated in depression. The current study explored the association between
depression symptomatology and SAT performance. Methods. Two exper-
iments presented participants with an SAT paradigm embedded within a
simple decision-making task, together with measures of depression symptoma-
tology. Experiment 1 (N = 349) was correlational, whereas Experiment 2 was
a two-phase experiment where participants (N = 501) were first pre-screened
on depression symptomatology and extreme-low and extreme-high responders
(total N = 91) were invited to Phase 2. Behavioural data were modelled with
a drift diffusion model. Results. Behavioural data and associated diffusion
modelling showed large and robust SAT effects. Emphasising accuracy led
to an increase in boundary separation, an increase in drift rate, and an
increase in non-decision time. However, the magnitude of the changes of
these parameters with SAT instructions were not associated with measures
of depression symptomatology. Conclusions. The results suggest that the
strategic adaptation of response settings in response to environmental changes
in speed–accuracy instructions do not appear to be associated with depression
symptomatology.
Keywords: Depression; Speed–accuracy tradeoff; Diffusion modelling
Word count: 4,476DEPRESSION & SPEED–ACCURACY TRADEOFFS 2
Depression is associated with a wide range of cognitive deficits (Burt, Zembar, &
Niederehe, 1995; McDermott & Ebmeier, 2009; Rock, Roiser, Riedel, & Blackwell, 2014)
including higher-level executive functioning (Snyder, 2013) which is essential for goal-directed
behaviour. One crucial aspect of goal-directed behaviour is the ability to accurately make
rapid decisions. Many tests of cognition and executive functioning require rapid decisions
from participants in the form of choice response time (RT) tasks. For example, in the task
switching paradigms (a test of cognitive flexibility; Grange & Houghton, 2014) participants
are presented with simple stimuli (e.g., digits) and asked to rapidly switch between two
or more simple tasks (e.g., is the stimulus odd or even / is the stimulus lower or higher
than five?). Although the switching element is of primary interest to cognitive (Kiesel et al.,
2010; Vandierendonck, Liefooghe, & Verbruggen, 2010) and clinical (e.g., Ravizza & Salo,
2014) researchers, rapid decision-making is embedded within the primary task. If depression
negatively impacts cognitive processes underlying successful decision making, this could
manifest itself in tests of executive functioning (Grange & Rydon-Grange, 2021; Lawlor et
al., 2020).
Rapid decision-making is an attractive area to study in depression and other clinical
disorders as rich theoretical developments have been made in the understanding of the
underlying cognitive processes (Ratcliff & McKoon, 2008). These theories have been devel-
oped into computational models allowing the researcher to quantify key latent parameters
reflecting the underlying cognitive processes (e.g., Voss, Nagler, & Lerche, 2013; Voss, Voss,
& Lerche, 2015). Of interest to the current study, researchers can explore the association
between these latent parameters and severity of clinical presentation (White, Ratcliff, Vasey,
& McKoon, 2010).
The current study explores the association between depression symtpomatology and
one essential aspect of decision-making: The ability to trade speed for accuracy. Such speed–
accuracy tradeoffs (SATs)—the finding that emphasisng speed produces poorer accuracy and
emphasising accuracy slows responding—are ubiquitous in the laboratory and everyday life
(Heitz, 2014) and suggest participants can adopt different response strategies depending on
current environmental demands. The question addressed here is to establish the association
between depression symptomatology and the ability to shift response strategies.
The paper is structured as follows. First, an overview of a popular computational
model of rapid decision-making (the drift diffusion model) is provided. I then discuss how
strategic adoption of different response strategies are accounted for by the model. I then
present the aims of the current study, and present two experiments probing the association
between depression symptomatology and shifts in response strategies.
All raw data, analysis scripts, and code for the modelling can be downloaded from https://osf.io/48xkh/
Correspondence concerning this article should be addressed to James A. Grange, School of Psychology,
Dorothy Hodgkin Building, Keele University, Keele, ST5 5BG, United Kingdom. E-mail: grange.jim@gmail.c
omDEPRESSION & SPEED–ACCURACY TRADEOFFS 3
The drift diffusion model (DDM)
The DDM (see Figure 1) assumes that a decision in an RT task is the result of noisy
accumulation of sensory evidence—modelled as a diffusion process—towards one of two
response boundaries (one reflecting the correct response and the other reflecting an error
response). Evidence accumulation continues until one of the response boundaries is met.
RT of the model is determined by how long the diffusion process took to reach one of the
response boundaries, and accuracy of the model is determined by whether the diffusion
process met the correct response boundary.
Figure 1 . Schematic overview of evidence accumulation in the drift diffusion model. See
text for details. Figure available at https://tiny.cc/diffusionmodel under CC license
https://creativecommons.org/licenses/by/2.0/. Note: ms = milliseconds.
The DDM has three main parameters. The drift rate represents the average rate of
evidence accumulation across trials, with higher values reflecting more rapid accumulation
leading to faster responding. The boundary separation reflects the height of the response
boundaries and determines how much evidence is required before a decision is made. Higher
boundary separation requires more evidence to be accumulated, prolonging RT relative to
lower boundary separation. However, lowering boundary separation increases the probability
of an error due to noise in the diffusion process. The non-decision parameter reflects elements
of the response unrelated to the decision-making process itself, such as perceptual encoding
of the stimulus and motoric responding.
The DDM has been successfully applied to a range of clinical questions (see White et
al., 2010 for brief review), including depression. Lawlor et al. (2020) applied the DDM to
assess the impact of Major Depressive Disorder (MDD) on decision making in two studies. InDEPRESSION & SPEED–ACCURACY TRADEOFFS 4
both, the researchers found that MDD was associated with reduced drift rates and increased
boundary separation compared to controls. This extended previous work which found
impaired evidence accumulation in MDD using a model of executive control (Dillon et al.,
2015; but see Grange & Rydon-Grange, 2021). These results suggest that slowed responding
in MDD can be explained by poorer evidence accumulation in the decision-making process
together with a more cautious mode of responding.
Speed-accuracy tradeoffs (SATs)
The higher boundary separation found in MDD could reflect the strategic adoption of a
more cautious mode of responding, but it could also reflect an inability of MDD participants
to engage in a faster (but more error-prone) mode of responding (for a similar argument
applied to healthy ageing, see Forstmann et al., 2011). This question cannot conclusively be
addressed without inducing SAT behaviour in participants. SATs can be induced in a variety
of ways (Heitz, 2014), including via explicit instructions to participants (e.g., in one block of
trials emphasise speed, and in another emphasise accuracy). The DDM accounts for SAT by
assuming an increase of the boundary separation occurs when accuracy is emphasised (cf.,
speed) thus demanding more evidence to be accumulated before committing to a response.
Studies have shown that emphasising speed over accuracy indeed leads to lower estimates of
the boundary separation parameter (see e.g., Forstmann et al., 2011; Ratcliff, Thapar, &
McKoon, 2007), but some studies have found that drift rates are also reduced (e.g., Rae,
Heathcote, Donkin, Averell, & Brown, 2014).
SATs and depression
Strategic adaptation of response boundary settings could be disrupted because of the
involvement of the striatum component of the basal ganglia in human cortex in SAT tasks,
a cortical area also implicated in depression. Neuroimaging has shown that speed-emphasis
leads to activation of the striatum as well as the pre-supplementary motor area, and that
individual variation in activation of these structures is associated with individual variation
in estimates of boundary separation parameters of a version of the DDM (Forstmann et al.,
2008, 2011). Research has shown reduced activity in striatal regions in MDD (Hamilton
et al., 2018) which can impact goal-directed behaviour due to the interface between the
striatum and pre-frontal regions of cortex (Marquand, Haak, & Beckmann, 2017). Indeed,
depression has been shown to be associated with a deficit in functional connectivity between
striatal and pre-frontal regions (Furman, Hamilton, & Gotlib, 2011; Hamilton et al., 2018;
Pan et al., 2017).
Given the link between the involvement of the striatum in SAT behaviour and
its disruption due to depression, examination of SAT performance—and formal DDM
modelling—could provide important insights into the cognitive profile of depression. Only
one study has examined the impact of depression on SAT performance together with DDM
application. Vallesi, Canalaz, Balestrieri, and Brambilla (2015) compared SAT performance
of 20 participants with a current or previous MDD diagnosis with 28 controls. Participants
performed a perceptual discrimination task and were cued on a trial-by-trial basis whether
to emphasise speed or accuracy in their response. Behavioural results showed the typical
SAT pattern of enhanced accuracy but longer RT on accuracy-emphasis trials, but it wasDEPRESSION & SPEED–ACCURACY TRADEOFFS 5
not reported whether this behavioural pattern interacted with depression grouping. The
DDM results showed an overall reduction of drift rate in the depression group (see also
Lawlor et al., 2020). The modelling also showed that accuracy-emphasis increased estimates
of boundary separation, although again it was not reported whether this was moderated
by depression grouping. However, the results did show that those with MDD maintained a
lower boundary separation after speed-emphasis trials compared to when the previous trial
emphasised accuracy, suggesting a deficit in strategically updating response settings after
emphasising accuracy.
The current study
The current study was designed to further examine the association between depression
and the ability to adapt response strategies to SAT instructions. Vallesi et al. (2015) cued
participants on a trial-by-trial basis whether to emphasise speed or accuracy; thus, their
study required frequent switching between response strategies, which could be disrupted by
the well-known deficit in task switching found in depression (Ravizza & Salo, 2014). Thus
it is not clear whether the findings of Vallesi et al. (2015) were caused by a deficit in the
ability to establish appropriate response strategies during SAT, or whether they were caused
by general deficits in cognitive flexibility.
Two experiments were conducted in the current study utilising an SAT paradigm
where speed- and accuracy-emphasis occurred in separate blocks. DDM was applied to the
data to explore the magnitude of changes in model parameters across speed- and accuracy-
emphasis blocks and whether these changes were associated with the magnitude of depression
symptomatology. This design allowed me to disentangle the contribution of task switching
and the overall ability to set appropriate response strategies (cf., Vallesi et al., 2015) because
the relevant speed–accuracy emphasis was constant within blocks.
Experiment 1 was correlational to assess whether changes in outcome variables across
speed–accuracy emphasis blocks was associated with the magnitude of depression symptoma-
tology. Experiment 2 was a between-groups design where a large number of participants
were first pre-screened on a measure of depression symtomatology, and individuals with
very-high and very-low scores were invited back to complete the SAT task.
Experiment 1
Method
The experiment was programmed and delivered online using Gorilla (Anwyl-Irvine,
Massonnié, Flitton, Kirkham, & Evershed, 2020); participants were recruited via Prolific
acadmic https://www.prolific.co/.
Participants. Participants residing in the UK or USA were recruited; the age-range
of the sample was restricted to 18–35 because RTs are known to slow due to healthy ageing
(Salthouse, 2000). If a wider age range was included it would be hard to find a single RT
threshold for the behavioural task that would be suitably challenging for all ages.
An a priori sensitivity analysis (Appendix A) suggested 300 participants would provide
over 86% power to detect the effect size of interest ((r = −0.18); Grange & Rydon-Grange,DEPRESSION & SPEED–ACCURACY TRADEOFFS 6
2021). 375 participants completed the study, but some were removed due to pre-specified
exclusion criteria. 2 were removed due to failing the attention check embedded within the
SHPS questionnaire. 17 were removed for failure to retain a session-wise accuracy rate
above 75% on the behavioural task. 6 were removed due to having more than 25% of their
responses slower than the RT threshold of 500ms. A further 1 participant was removed due
to an error with the experimental programme. Behavioural analysis was conducted on 349
participants. 5 participants were removed from the DDM analysis as the model did not fit
their data well.
Materials.
Quick inventory of depressive symptomatology–self-report (QIDS–SR).
The 16-item self-report version of the Quick Inventory of Depressive Symptomatology,
QIDS-SR-16 (Rush et al., 2003), was used. The QIDS has been shown to have excellent
internal consistency and validity (Rush et al., 2003). It consists of 16 items assessing the
severity of depressive symptoms experienced by participants in the preceding seven days.
Items relate to particular aspects of depression symptoms (e.g., “views of self”) and requires
participants to select one response from four options that best represents their experience
(e.g., “I am more self-blaming than usual”).
Snaith–Hamilton pleasure scale (SHPS). The SHPS is a 14-item self-report
questionnaire measuring the participants’ ability to experience pleasure “. . . over the last few
days” (Snaith et al., 1995). Participants were required to read a statement (e.g., “I would
enjoy seeing other people’s smiling faces”) and indicate their agreement with this statement
on a four-point scale (Definitely Agree; Agree; Disagree; Strongly Disagree). The scoring
method recommended by Franken, Rassin, and Muris (2007) was used.
Attention check. An attention check was embedded within the SHPS to identify
participants who were not paying attention to the items. It read “It is important that you
pay attention to this study. Please select Disagree”. Participants who did not select this
response were deemed to have failed the attention check and were removed before analysis.
Behavioural Task. The behavioural task developed by Lawlor et al. (2020) was
utilised. The task presents participants with a schematic face within the centre of a square
frame in the centre of the screen. The participants’ task was to judge whether the mouth was
“long” or “short”. During the instruction phase, participants were informed that the “short”
mouth lies entirely within the boundaries of the eyes, whereas the “long” mouth extends to
the edge of the eyes. Each trial began with a black fixation cross presented centrally for
500ms. After this, a schematic face minus the mouth appeared for 500 ms, at which point
the mouth was presented for 100 ms. After this, the mouth disappeared, and the schematic
face remained on screen until a participant made a response. Participants were required to
press the “Z” key if the mouth was short, and the “M” key if the mouth was long.
The behavioural task was presented in stages to help the participants learn the task,
and then to acclimatise to the speed—accuracy instructions. Participants were first given
instructions on how to perform the mouth-length task which asked participants to respond
“. . . as quickly and as accurately as possible”. After these instructions, participants completed
a 16-trial practice block. In this practice block, participants were given feedback on the
accuracy of their responses to help them learn the task. If the participant made a correct
response, a green tick was presented centrally for 200ms; if the participant made an error, a
red cross was presented centrally for 200ms.DEPRESSION & SPEED–ACCURACY TRADEOFFS 7
After this initial practice, participants were given instructions about the speed–accuracy
emphasis instructions. They were told that they would experience “accuracy-emphasis”
and “speed-emphasis” blocks, and that in accuracy-emphasis blocks, they should respond
“as accurately as possible without taking too long to respond”. They were told that in
speed-emphasis blocks they should respond “as quickly as possible without making too many
mistakes. DO NOT GUESS”. After these instructions, participants were presented with a
practice session consisting of 12 trials under accuracy-emphasis instructions followed by 12
trials under speed-emphasis instructions.
Response feedback on these trials changed depending on which instructions they
were performing under. In accuracy-emphasis trials, if participants made an error they
saw the message “INCORRECT!” presented centrally in red font for 1500ms. On speed
trials, if participants responded slower than 500ms they saw the message “TOO SLOW!”
for 1500ms. 500ms was selected as an appropriate threshold to initiate speed feedback
based on performance in a pilot study (see Pilot Study 1 in Appendix B). No feedback was
provided for correct responses in accuracy-emphasis blocks, and no feedback was provided
for sub-threshold responses in speed-emphasis blocks. (See Pilot Study 2 in Appendix B for
evidence these instructions produce the desired behavioural effects.)
After this second practice, participants were then presented with 8 blocks of 48 trials
with self-paced rest screens between each block. The relevant emphasis (speed vs. accuracy)
alternated every block, beginning with accuracy emphasis.
Procedure. Upon entering the study online via Prolific, participants provided
informed consent, and then completed a short demographics questionnaire. After this, the
experimental software randomly allocated the participant to one of four presentation orders
of study elements: (1) behavioural task–QIDS–SHPS; (2) behavioural task–SHPS–QIDS; (3)
QIDS–SHPS–behavioural task; or (4) SHPS–QIDS–behavioural task. Upon completion of
all stages, participants were presented with a debrief screen providing detailed information
of the study.
Quality checks and data exclusion. Participants who failed the attention checks
were removed from the analysis. Participants were also removed whose mean accuracy
performance across the experiment was below 75%. For response time analysis, error trials
were removed, and correct response times were trimmed to only include RTs longer than
150ms and shorter than 5,000ms. For the diffusion modelling, participants whose data were
not fit well by the model were removed.
Results
All of the analysis utilised R (Version 4.0.2; R Core Team, 2020)1 ,
1
We, furthermore, used the R-packages brms (Version 2.13.0; Bürkner, 2017, 2018), dplyr (Version 1.0.6;
Wickham et al., 2021), forcats (Version 0.5.0; Wickham, 2020), ggplot2 (Version 3.3.3; Wickham, 2016), here
(Version 1.0.1; Müller, 2020), MASS (Version 7.3.51.6; Venables & Ripley, 2002), papaja (Version 0.1.0.9997;
Aust & Barth, 2020), purrr (Version 0.3.4; Henry & Wickham, 2020), Rcpp (Version 1.0.7; Eddelbuettel &
François, 2011; Eddelbuettel & Balamuta, 2018), readr (Version 1.3.1; Wickham, Hester, & Francois, 2018),
rtdists (Version 0.11.2; Singmann, Brown, Gretton, & Heathcote, 2020), stringr (Version 1.4.0; Wickham,
2019), tibble (Version 3.1.1; Müller & Wickham, 2021), tidyr (Version 1.1.3; Wickham, 2021), tidyverse
(Version 1.3.0; Wickham, Averick, et al., 2019), and tinylabels (Version 0.1.0; Barth, 2020).DEPRESSION & SPEED–ACCURACY TRADEOFFS 8
Questionnaire scores. There was good variability of scores in the QIDS ques-
tionnaire (range = 0–24, M=9.08, SD=5.29) and the SHPS questionnaire (range = 14–48,
M=27.71, SD=6.21). In the QIDS, 77 participants had scores greater than or equal to 14,
and 154 had scores lower than 8, which were the thresholds used by Dillon et al. (2015)
for inclusion in their major depression and control groups, respectively. Bayesian linear
regression confirmed that QIDS scores were predicted by SHPS scores, b = 0.47, 95% credible
interval (CI; 0.39, 0.54).
Behavioural data. For the response time analysis, error trials were removed. Cor-
rect RTs were trimmed to only include RTs longer than 150ms and shorter than 5,000ms. The
analysis of the behavioural data showed the typical effects for speed–accuracy tradeoff instruc-
tions: Proportion accuracy was significantly higher in accuracy-emphasis blocks (M=0.944,
SE=0.002) than in the speed-emphasis blocks (M=0.860, SE=0.005), t(348)=20.26, pTable 1
Posterior estimates from the Bayesian linear regressions predicting various outcome measures from from QIDS and
SHPS scores. Note that for the beta estimates, 95% Credible Intervals are provided in square brackets.
QIDS SHPS
Source Outcome Intercept beta Intercept beta
Behavioural Accuracy 0.904 0.000 [-0.001, 0.001] 0.899 0.000 [-0.001, 0.001]
RT 398 0.413 [-0.454, 1.265] 397 0.206 [-0.529, 0.950]
delta-Accuracy 0.081 0.000 [-0.001, 0.002] 0.092 0.000 [-0.002, 0.001]
delta-RT 36 0.720 [-0.131, 1.549] 34 0.288 [-0.443, 1.016]
DDM Boundary Separation 0.774 0.001 [-0.003, 0.005] 0.793 0.000 [-0.004, 0.003]
Drift Rate 4.273 -0.016 [-0.039, 0.008] 4.542 -0.015 [-0.035, 0.005]
Non-Decision Time 0.302 0.000 [0.000, 0.001] 0.298 0.000 [0.000, 0.001]
delta-Boundary Separation 0.144 0.005 [-0.002, 0.012] 0.208 -0.001 [-0.006, 0.005]
delta-Drift Rate 0.901 0.011 [-0.018, 0.039] 0.889 0.004 [-0.020, 0.028]
delta-Non-Decision Time 0.039 0.000 [-0.001, 0.001] 0.037 0.000 [-0.001, 0.001]
Note. DDM = Drift diffusion model. RT = Response time.
DEPRESSION & SPEED–ACCURACY TRADEOFFS
9DEPRESSION & SPEED–ACCURACY TRADEOFFS 10
Diffusion modelling. The diffusion model was fit to individual participant data
using the fast-dm-30 using Kolmogorov-Smirnov parameter optimisation. Boundary sepa-
ration, drift rate, and non-decision time were allowed to freely vary between speed–accuracy
emphasis conditions; all other parameters were either fixed to zero or were not free to vary
across conditions (see Voss et al., 2015). 5 participants who showed a significant deviation
between observed and model-predicted distributions were removed from inferential analysis.
For the remaining participants, the diffusion model provided a good fit to the observed data
(see Appendix C).
Results showed that estimates of boundary separation were higher in accuracy-
emphasis blocks (M = 0.876, SE = 0.017) than in the speed-emphasis blocks (M=0.689,
SE=0.009), t(343)=10.54, pDEPRESSION & SPEED–ACCURACY TRADEOFFS 11
Overall Boundary Separation Overall Drift Rate Overall Non−Decision Time
10.0
0.40
1.5
7.5
Parameter Value
Parameter Value
Parameter Value
0.35
1.0 5.0 0.30
0.25
2.5
0.5
0.20
0 5 10 15 20 25 0 5 10 15 20 25 0 5 10 15 20 25
QIDS QIDS QIDS
Delta Boundary Separation Delta Drift Rate Delta Non−Decision Time
2 0.4
4
1
Delta Value
Delta Value
Delta Value
0.2
0
0
0.0
−1 −4
−2
−0.2
0 5 10 15 20 25 0 5 10 15 20 25 0 5 10 15 20 25
QIDS QIDS QIDS
Figure 2 . Results of the Bayesian linear regression for overall (i.e., averaged across accuracy-
and speed-emphasis blocks) diffusion model parameters (Upper Row) and delta parameter
values (Lower Row) predicted from QIDS scores. Points represent individual participant
data, and the overlaid lines represent draws from the posterior distribution of the statistical
model.
150ms and shorter than 5,000ms. For the diffusion modelling, participants whose data were
not fit well by the model were removed.
Results
Questionnaire scores. The mean QIDS score for the low-depression group was
2.00 (SE = 0.19, range = 0–5) and for the high-depression group was 17.19 (SE = 0.44, range
= 10–22). The mean SHPS score for the low-depression group was 22.35 (SE = 0.78, range
= 14–34) and for the high-depression group was 33.69 (SE = 0.96, range = 23–52). Bayesian
linear regression confirmed that QIDS scores were predicted by SHPS scores in Phase 2,
b = 0.70, 95%-CI (0.56, 0.84). The product-moment correlation between questionnaire scores
in Phase 1 and Phase 2 for the QIDS was r=0.985.
Behavioural data. The behavioural data is summarised in Figure 3. Two separate
mixed factorial analyses of variance (ANOVA) were performed on the RT and accuracyDEPRESSION & SPEED–ACCURACY TRADEOFFS 12
data. The ANOVA summary table for all analyses are shown in Table 2. Accuracy-emphasis
blocks led to a significant increase in both accuracy and RT. There was no main effect of
depression group, and—critical to the current study’s aims—no interaction.
Observed Response Time Observed Accuracy
0.96
group group
420 high high
low low
0.93
Response Time (ms)
Proportion Accuracy
400
0.90
0.87
380
0.84
accuracy_trials speed_trials accuracy_trials speed_trials
Condition Condition
Figure 3 . Observed mean response time (in milliseconds, ms) and proportion accuracy for
both low and high depression groups as a function of speed–accuracy emphasis condition.
Error bars denote one standard error around the mean.
Diffusion modelling. Model fitting was conducted as in Experiment 1. 3 partici-
pants (two from the low-depression group and one from the high-depression group) were
removed from analyses as their data were not fit well by the model (see Appendix C). The
results of the diffusion model analysis are shown in Figure 4 and the ANOVA summary
table for all analyses are shown in Table 2. Accuracy-emphasis blocks led to a significant
increase in boundary separation, in drift rate, and in non-decision time. There was no main
effect of depression group for any of the DDM parameters, and—critically to the current
study’s aims—no interaction.
General Discussion
The current study examined the association between depression symptomatology and
the ability to adopt different response strategies in response to speed–accuracy tradeoff
instructions. Given the role of the striatum component of the basal ganglia in human cortex
in adapting response strategies (Forstmann et al., 2008, 2011), and given the reported
reduced activity of this cortical region in depression (Furman et al., 2011; Hamilton et al.,
2018; Pan et al., 2017), the SAT paradigm becomes an important paradigm with which to
probe the cognitive profile of depression. In addition, the drift diffusion model (Voss et al.,
2015) was used to quantify changes in latent cognitive parameters of decision-making, and
to assess whether the magnitude of changes in these parameters with SAT instructions were
associated with depression symptomatology.
The results showed no evidence for an association between depression symtpomatologyDEPRESSION & SPEED–ACCURACY TRADEOFFS 13
Boundary Separation (a) Drift Rate (v) Non−Decision Time (t0)
1.1 5.0 0.32
group
high
low 0.31
1.0
Mean Parameter Value
Mean Parameter Value
Mean Parameter Value
4.5
0.30
0.9
4.0 0.29
0.8
0.28
3.5
0.7
0.27
accuracy speed accuracy speed accuracy speed
Condition Condition Condition
Figure 4 . Mean diffusion model parameter estimates for both low and high depression
groups as a function of speed–accuracy emphasis condition. Error bars denote one standard
error around the mean.
Table 2
ANOVA summary table for Experiment 2 for the behavioural data and parameters of the drift
diffusion model (DDM).
Source Outcome Effect F-value p Effect Size
Behavioural Accuracy Depression Grouping (D) 0.66 .418 .006
SAT Condition (S) 176.00DEPRESSION & SPEED–ACCURACY TRADEOFFS 14
and SAT performance. This was true for both the behavioural data (i.e., changes in RT and
accuracy) and the DDM parameters: Typical behavioural SAT effects were found across
both experiments suggesting the experimental instructions were successful in inducing large
effects on response strategies; the DDM fitting (which produced excellent fits to the data)
showed that emphasising accuracy (cf., speed) led to an increase in the boundary separation
parameter, a reduction in drift rate, and an increase in non-decision time (cf., Rae et al.,
2014), but the magnitude of changes in these parameters was not associated with depression
symptom severity.
These results appear to stand in contrast to previous research. For example, Vallesi
et al. (2015)—in a study comparing MDD patients with controls on an SAT paradigm—
found some evidence for deficits in the MDD group on updating the boundary separation
parameter immediately after speed-emphasis trials. This suggests that this group struggled
to adapt their response strategy to a more cautious mode of responding after emphasising
speed. However, because this study utilised trial-by-trial cuing of—and hence required
rapid switching between—different speed–accuracy strategies, there is the potential that
the well-known deficit in task switching found in depression (e.g., Ravizza & Salo, 2014;
Snyder, 2013) contributed to the deficit observed by Vallesi et al. (2015). That is, the
finding of Vallesi et al. (2015) could be due to problems with rapid shifting of response
strategies, a problem with general task switching, or both. The current study removed the
requirement for frequent switching between response strategies by separating speed- and
accuracy-emphasis instructions into separate blocks. A fruitful avenue for future research
would be to compare performance on within-block switching of response strategies (i.e., like
Vallesi et al., 2015) and between-block switching of response strategies (like the study) in
the same sample to explore the contribution of task switching.
Limitations
The age-range of our samples was restricted to 18–35 so that a single response time
deadline could be used in speed-emphasis trials (see Appendix B) without unduly penalising
older adults (due to the well-documented general slowing found in older adults, Salthouse,
2000); it remains possible therefore that the lack of an association between SAT performance
and depression symptomatology on holds for this age range. Although the use of a single
global response deadline is found in many SAT procedures (see Rae et al., 2014), future
research could address the impact of this by utilising individually tailored response deadlines
for each participant via a tracking procedure (see Heitz, 2014).
Another limitation is that the current study did not use a clinical sample; however,
both experiments showed good variability in depression symptom severity as measured by
the QIDS. In Experiment 1, 22.06% of the sample had a QIDS score of 14 or more, which
was the cutoff used by Dillon et al. (2015) as an inclusion criterion to their depression
condition. In Experiment 2, this concern was directly addressed by pre-screening a large
number of participants on depression severity and only inviting back the extreme-low and
extreme-high scorers on the QIDS to take part in the SAT paradigm. An advantage of
recruiting non-clinical samples is that the sample size of both of the studies reported here
were large. Indeed, Experiment 1 had over 86% power to detect even a small association
between depression and SAT performance (see Appendix A). Thus although the data provideDEPRESSION & SPEED–ACCURACY TRADEOFFS 15 compelling evidence for a lack of an association, it would be prudent to replicate this study with a clinical sample
DEPRESSION & SPEED–ACCURACY TRADEOFFS 16
References
Anwyl-Irvine, A. L., Massonnié, J., Flitton, A., Kirkham, N., & Evershed, J. K. (2020).
Gorilla in our midst: An online behavioral experiment builder. Behavior Research
Methods, 52 (1), 388–407. https://doi.org/10.3758/s13428-019-01237-x
Aust, F., & Barth, M. (2020). papaja: Prepare reproducible APA journal articles with R
Markdown. Retrieved from https://github.com/crsh/papaja
Barth, M. (2020). Tinylabels: Lightweight variable labels. Retrieved from https://CRAN.R-
project.org/package=tinylabels
Burt, D. B., Zembar, M. J., & Niederehe, G. (1995). Depression and memory impairment: A
meta-analysis of the association, its pattern, and specificity. Psychological Bulletin,
117 (2), 285–305. https://doi.org/10.1037/0033-2909.117.2.285
Bürkner, P.-C. (2017). brms: An R package for Bayesian multilevel models using Stan.
Journal of Statistical Software, 80 (1), 1–28. https://doi.org/10.18637/jss.v080.i01
Bürkner, P.-C. (2018). Advanced Bayesian multilevel modeling with the R package brms.
The R Journal, 10 (1), 395–411. https://doi.org/10.32614/RJ-2018-017
Dillon, D. G., Wiecki, T., Pechtel, P., Webb, C., Goer, F., Murray, L., . . . Pizzagalli, D. A.
(2015). A computational analysis of flanker interference in depression. Psychological
Medicine, 45 (11), 2333–2344. https://doi.org/10.1017/S0033291715000276
Eddelbuettel, D., & Balamuta, J. J. (2018). Extending extitR with extitC++: A Brief
Introduction to extitRcpp. The American Statistician, 72 (1), 28–36. https://doi.or
g/10.1080/00031305.2017.1375990
Eddelbuettel, D., & François, R. (2011). Rcpp: Seamless R and C++ integration. Journal
of Statistical Software, 40 (8), 1–18. https://doi.org/10.18637/jss.v040.i08
Forstmann, B. U., Dutilh, G., Brown, S., Neumann, J., Cramon, D. Y. von, Ridderinkhof, K.
R., & Wagenmakers, E.-J. (2008). Striatum and pre-SMA facilitate decision-making
under time pressure. Proceedings of the National Academy of Sciences, 105 (45),
17538–17542. https://doi.org/10.1073/pnas.0805903105
Forstmann, B. U., Tittgemeyer, M., Wagenmakers, E.-J., Derrfuss, J., Imperati, D., &
Brown, S. (2011). The speed-accuracy tradeoff in the elderly brain: A structural
model-based approach. Journal of Neuroscience, 31 (47), 17242–17249. https:
//doi.org/10.1523/JNEUROSCI.0309-11.2011
Franken, I. H. A., Rassin, E., & Muris, P. (2007). The assessment of anhedonia in clinical
and non-clinical populations: Further validation of the SnaithHamilton Pleasure
Scale (SHAPS). Journal of Affective Disorders, 99 (1), 83–89. https://doi.org/10.101
6/j.jad.2006.08.020
Furman, D. J., Hamilton, J. P., & Gotlib, I. H. (2011). Frontostriatal functional connectivity
in major depressive disorder. Biology of Mood & Anxiety Disorders, 1 (1), 11.
https://doi.org/10.1186/2045-5380-1-11DEPRESSION & SPEED–ACCURACY TRADEOFFS 17
Grange, J. A., & Houghton, G. (Eds.). (2014). Task Switching and Cognitive Control. New
York, NY: Oxford University Press.
Grange, J. A., & Rydon-Grange, M. (2021). Computational modelling of attentional
selectivity in depression reveals perceptual deficits. Psychological Medicine, 1–10.
https://doi.org/10.1017/S0033291720002652
Hamilton, J. P., Sacchet, M. D., Hjørnevik, T., Chin, F. T., Shen, B., Kämpe, R., . . . Gotlib,
I. H. (2018). Striatal dopamine deficits predict reductions in striatal functional
connectivity in major depression: A concurrent 11C-raclopride positron emission
tomography and functional magnetic resonance imaging investigation. Translational
Psychiatry, 8 (1), 1–10. https://doi.org/10.1038/s41398-018-0316-2
Heitz, R. P. (2014). The speed-accuracy tradeoff: History, physiology, methodology, and
behavior. Frontiers in Neuroscience, 8. https://doi.org/10.3389/fnins.2014.00150
Henry, L., & Wickham, H. (2020). Purrr: Functional programming tools. Retrieved from
https://CRAN.R-project.org/package=purrr
Kiesel, A., Steinhauser, M., Wendt, M., Falkenstein, M., Jost, K., Philipp, A. M., & Koch, I.
(2010). Control and interference in task switchingA review. Psychological Bulletin,
136 (5), 849–874. https://doi.org/10.1037/a0019842
Kruschke, J. K., & Liddell, T. M. (2018). The bayesian new statistics: Hypothesis testing, esti-
mation, meta-analysis, and power analysis from a bayesian perspective. Psychonomic
Bulletin & Review, 25 (1), 178–206. https://doi.org/10.3758/s13423-016-1221-4
Lawlor, V. M., Webb, C. A., Wiecki, T. V., Frank, M. J., Trivedi, M., Pizzagalli, D. A.,
& Dillon, D. G. (2020). Dissecting the impact of depression on decision-making.
Psychological Medicine, 50 (10), 1613–1622. https://doi.org/10.1017/S00332917190
01570
Marquand, A. F., Haak, K. V., & Beckmann, C. F. (2017). Functional corticostriatal
connection topographies predict goal-directed behaviour in humans. Nature Human
Behaviour, 1 (8), 1–9. https://doi.org/10.1038/s41562-017-0146
McDermott, L. M., & Ebmeier, K. P. (2009). A meta-analysis of depression severity and
cognitive function. Journal of Affective Disorders, 119 (1), 1–8. https://doi.org/10.1
016/j.jad.2009.04.022
Müller, K. (2020). Here: A simpler way to find your files. Retrieved from https://CRAN.R-
project.org/package=here
Müller, K., & Wickham, H. (2021). Tibble: Simple data frames. Retrieved from https:
//CRAN.R-project.org/package=tibble
Pan, P. M., Sato, J. R., Salum, G. A., Rohde, L. A., Gadelha, A., Zugman, A., . . . Stringaris,
A. (2017). Ventral striatum functional connectivity as a predictor of adolescent
depressive disorder in a longitudinal community-based sample. American Journal of
Psychiatry, 174 (11), 1112–1119. https://doi.org/10.1176/appi.ajp.2017.17040430DEPRESSION & SPEED–ACCURACY TRADEOFFS 18
Rae, B., Heathcote, A., Donkin, C., Averell, L., & Brown, S. (2014). The hare and the
tortoise: Emphasizing speed can change the evidence used to make decisions. Journal
of Experimental Psychology. Learning, Memory, and Cognition, 40 (5), 1226–1243.
https://doi.org/10.1037/a0036801
Ratcliff, R., & McKoon, G. (2008). The diffusion decision model: Theory and data for
two-choice decision tasks. Neural Computation, 20 (4), 873–922. https://doi.org/10.1
162/neco.2008.12-06-420
Ratcliff, R., Thapar, A., & McKoon, G. (2007). Application of the diffusion model to
two-choice tasks for adults 75–90 years old. Psychology and Aging, 22 (1), 56–66.
https://doi.org/10.1037/0882-7974.22.1.56
Ratcliff, R., Thapar, A., & McKoon, G. (2010). Individual differences, aging, and IQ in
two-choice tasks. Cognitive Psychology, 60 (3), 127–157. https://doi.org/10.1016/j.
cogpsych.2009.09.001
Ravizza, S. M., & Salo, R. E. (2014). Task switching in psychiatric disorders. In J. A.
Grange & G. Houghton (Eds.), Task Switching and Cognitive Control (pp. 300–331).
New York, NY: Oxford University Press.
R Core Team. (2020). R: A language and environment for statistical computing. Vienna,
Austria: R Foundation for Statistical Computing. Retrieved from https://www.R-
project.org/
Rock, P. L., Roiser, J. P., Riedel, W. J., & Blackwell, A. D. (2014). Cognitive impairment in
depression: A systematic review and meta-analysis. Psychological Medicine, 44 (10),
2029–2040. https://doi.org/10.1017/S0033291713002535
Rush, A. J., Trivedi, M. H., Ibrahim, H. M., Carmody, T. J., Arnow, B., Klein, D. N., . . .
Keller, M. B. (2003). The 16-Item quick inventory of depressive symptomatology
(QIDS), clinician rating (QIDS-C), and self-report (QIDS-SR): A psychometric
evaluation in patients with chronic major depression. Biological Psychiatry, 54 (5),
573–583. https://doi.org/10.1016/S0006-3223(02)01866-8
Salthouse, T. A. (2000). Aging and measures of processing speed. Biological Psychology,
54 (1), 35–54. https://doi.org/10.1016/S0301-0511(00)00052-1
Singmann, H., Brown, S., Gretton, M., & Heathcote, A. (2020). Rtdists: Response time
distributions. Retrieved from https://CRAN.R-project.org/package=rtdists
Snaith, R. P., Hamilton, M., Morley, S., Humayan, A., Hargreaves, D., & Trigwell, P. (1995).
A Scale for the Assessment of Hedonic Tone the SnaithHamilton Pleasure Scale. The
British Journal of Psychiatry, 167 (1), 99–103. https://doi.org/10.1192/bjp.167.1.99
Snyder, H. R. (2013). Major depressive disorder is associated with broad impairments
on neuropsychological measures of executive function: A meta-analysis and review.
Psychological Bulletin, 139 (1), 81–132. https://doi.org/10.1037/a0028727
Vallesi, A., Canalaz, F., Balestrieri, M., & Brambilla, P. (2015). Modulating speed-accuracy
strategies in major depression. Journal of Psychiatric Research, 60, 103–108. https:DEPRESSION & SPEED–ACCURACY TRADEOFFS 19
//doi.org/10.1016/j.jpsychires.2014.09.017
Vandierendonck, A., Liefooghe, B., & Verbruggen, F. (2010). Task switching: Interplay of
reconfiguration and interference control. Psychological Bulletin, 136 (4), 601–626.
https://doi.org/10.1037/a0019791
Venables, W. N., & Ripley, B. D. (2002). Modern applied statistics with s (Fourth). New
York: Springer. Retrieved from http://www.stats.ox.ac.uk/pub/MASS4
Voss, A., Nagler, M., & Lerche, V. (2013). Diffusion models in experimental psychology: A
practical introduction. Experimental Psychology, 60 (6), 385–402. https://doi.org/10
.1027/1618-3169/a000218
Voss, A., Voss, J., & Lerche, V. (2015). Assessing cognitive processes with diffusion
model analyses: A tutorial based on fast-dm-30. Frontiers in Psychology, 6, 336.
https://doi.org/10.3389/fpsyg.2015.00336
White, C. N., Ratcliff, R., Vasey, M. W., & McKoon, G. (2010). Using diffusion models to
understand clinical disorders. Journal of Mathematical Psychology, 54 (1), 39–52.
https://doi.org/10.1016/j.jmp.2010.01.004
Wickham, H. (2016). Ggplot2: Elegant graphics for data analysis. Springer-Verlag New
York. Retrieved from https://ggplot2.tidyverse.org
Wickham, H. (2019). Stringr: Simple, consistent wrappers for common string operations.
Retrieved from https://CRAN.R-project.org/package=stringr
Wickham, H. (2020). Forcats: Tools for working with categorical variables (factors). Re-
trieved from https://CRAN.R-project.org/package=forcats
Wickham, H. (2021). Tidyr: Tidy messy data. Retrieved from https://CRAN.R-project.or
g/package=tidyr
Wickham, H., Averick, M., Bryan, J., Chang, W., McGowan, L. D., François, R., . . . Yutani,
H. (2019). Welcome to the tidyverse. Journal of Open Source Software, 4 (43), 1686.
https://doi.org/10.21105/joss.01686
Wickham, H., François, R., Henry, L., & Müller, K. (2021). Dplyr: A grammar of data
manipulation. Retrieved from https://CRAN.R-project.org/package=dplyr
Wickham, H., Hester, J., & Francois, R. (2018). Readr: Read rectangular text data. Retrieved
from https://CRAN.R-project.org/package=readrDEPRESSION & SPEED–ACCURACY TRADEOFFS 20
Appendix A - Sample Size Planning
To determine the sample size for Experiment 1, the procedure introduced by Kruschke
and Liddell (2018) was employed. The procedure has several steps (visualised schematically
in Figure 5) which I outline below.
First, a minimum effect size of interest needs to be established. For the current
study, the research question is primarily interested in the relationship between depression
severity and various behavioural outcome variables and diffusion model parameters. I used
a correlation coefficient of r = −0.18 as the effect size of interest, as this is the correlation
found in our previous work (Grange & Rydon-Grange, 2021).
Second, idealised data that conform to this effect size of interest are simulated. For
this, I generated data from 250 simulated participants by sampling from a multivariate
normal distribution. I sampled data from two measures (each standardized with mean
zero and standard deviation of one) assuming the population-level correlation between the
measures was r = −0.18. This simulated data is shown in the left panel of Figure 5.
Idealised Data Simulated Data Predictor Posterior Distribution
2
6
2
Dependent Variable Value
Dependent Variable Value
1
4
density
0
0
−1
2
−2
−2
−3 0
−3 −2 −1 0 1 2 3 −2 0 2 −0.4 −0.3 −0.2 −0.1 0.0
Predictor Value Predictor Value Beta Value
Figure 5 . Schematic example of the steps applied to conduct sample size planning for
Bayesian linear regression. See text for details.
Then, the statistical model of choice is applied to this simulated data set. The
analytical approach in the current study was a multilevel Bayesian regression with one
outcome variable and one predictor variable. The posterior distribution of this statistical
model provides a set of plausible values for the main statistical parameters of interest (in
this case, the main parameter is the slope of the predictor variable). This is shown by the
blue lines in the left panel of Figure 5; each line represents a regression slope drawn from
the posterior of the Bayesian model. Each regression line is a plausible estimate of the true
population-level relationship between the two variables.
Then, new sample data is generated using these plausible parameter values. I generated
new samples of participants using 1,000 parameters from the posterior distribution of the
model fit to the idealised data. Each sample had N = 300 simulated participants, which was
the sample size I wished to assess the adequacy of in this analysis. For example, the red
line highlighted in the left panel of Figure 5 shows one draw from the posterior distribution;
this draw has a slope of -0.176. We would therefore simulate new data (again from theDEPRESSION & SPEED–ACCURACY TRADEOFFS 21
multivariate normal distribution) but use r = -0.176 as the correlation parameter. This new
simulated data is shown in the middle panel of Figure 5.
With this new sample data in hand, a new Bayesian regression model is fit to the
simulated data. Plausible regression lines from the posterior prediction of this new model fit
are shown as blue lines in the middle panel of Figure 5.
We can then assess the posterior distribution of the key parameter of interest; in the
current case, the key parameter is the slope of the regression (i.e., the beta value for the
predictor). The right panel of Figure 5 shows the posterior distribution of the predictor in
the regression fit. We are interested in whether our research goals have been met in this
posterior distribution. Specifically, we assess whether 95% of the posterior distribution is
different from zero. In the right panel of Figure 5, the 95% Credible Interval (CI) of the
posterior is shown as the horizontal black line. As can be seen, the entirety of the CI is
below zero; in this case, we would conclude that the true parameter value is non-zero. We
would therefore conclude that we have detected an effect.
After this, a new sample from the posterior of the idealised data model fit is selected
(i.e., a new red line is chosen) to generate new sample data, and the posterior distribution
from the model fit to the new sample data is examined to see whether the 95% CI is non-zero,
and so on (for a total of 1,000 iterations). The power analysis then counts the proportion of
sample data where the CI was non-zero.
If the intended sample size is sufficient, a true effect should be found in a large
proportion of simulated data sets. In our power analysis (assuming r = −0.18 and N = 300),
86.2% of simulated samples had a non-zero CI. Put another way, the intended sample size of
300 had a power of 86.2%.DEPRESSION & SPEED–ACCURACY TRADEOFFS 22
Appendix B - Pilot Studies
Pilot 1: Establishing Baseline Performance
In speed–accuracy tradeoff experiments, researchers typically reinforce accuracy and
speed instructions with performance feedback on a trial’s accuracy or speed (respectively)
when a particular criterion is not met. For example, in accuracy-emphasis blocks researchers
will typically present feedback (e.g., “ERROR!”) when an incorrect response is made. In
speed-emphasis blocks, feedback is provided (e.g., “TOO SLOW!”) when the participant’s
response is slower than a pre-determined threshold.The question thus arises of what response
time threshold should be used.
The purpose of the current pilot study was to establish baseline task performance in
terms of speed and accuracy on the task without speed- or accuracy-emphasis instructions;
with this baseline performance in hand, we can then use the pilot data to establish a sensible
response time threshold for speed-emphasis blocks in the main speed–accuracy tradeoff
Experiments.
Method.
Participants. 50 participants were recruited via Prolific Academic residing in the
UK or USA. I used pre-screening tools within Prolific Academic to limit our sample age
range to 18–35. I maintained a tight (and young) age-range because response times are
known to slow due to healthy ageing (Salthouse, 2000). If a wider age range was included in
the sample it would be hard to find a single response time threshold that would be suitably
challenging for older and younger participants alike. Participants could only take part via a
laptop or desktop computer.
Stimuli. The same stimuli used in the behavioural task reported in the main paper
was used in this first pilot study. See Figure 6 for a schematic overview of a single trial in
this task.
Figure 6 . Schematic overview of a single trial in the behavioural task. Note that images are
not to scale.
Procedure. After providing informed consent, the participants were presented with
full written instructions on how to perform the task. Participants then engaged in some
practice trials before moving on to the main experimental blocks. On each trial, a blackDEPRESSION & SPEED–ACCURACY TRADEOFFS 23
fixation cross was presented in the centre of the frame for 500ms. After this, the schematic
face was presented without the mouth for 500ms, at which point the mouth was presented
for 100ms. After this time, the mouth disappeared, and the schematic face remained on
screen until the participant made a response. The stimulus was selected randomly on each
trial with the constraint that there were equal numbers of long- and short-mouth stimuli
within each block.
The participants were required to press the “Z” key if they thought the presented
mouth was short, and “M” if they thought the presented mouth was long. Response
instructions asked participants to respond as quickly and as accurately as possible as soon as
the mouth appeared, and were asked to use the index finger of each hand for the responses.
Once a response was registered, the face was removed and the fixation cross for the next
trial appeared. During the practice block, participants received feedback on the accuracy of
their responses to help them learn the task. If the response was correct, a green tick was
presented on top of the schematic face; if the response was incorrect, a red “X” was shown.
Feedback was displayed for 200ms.
Participants were presented with 24 practice trials. The main experimental section
consisted of 6 blocks of 48 trials. There was the opportunity for a self-paced break between
each block. Once all blocks were complete, participants were presented with a debrief screen
informing them of the purpose of the study.
Quality checks and data exclusion. As the purpose of this pilot study was to
establish typical performance in this paradigm, I did not perform any quality checks or
exclude any data for the accuracy analysis. Trials in which an error was made were removed
from the response time analysis.
Results. Density distributions of both the mean proportion accuracy and mean
response times are shown in Figure 7. Mean proportion accuracy was 0.935 (SE = 0.007),
and mean response time was 453ms (SE = 11ms).
Establishing a suitable threshold. Examination of the response time density
distribution in Figure 7 suggests that the majority of response times fell below 500ms.
Indeed, the mean percentage of trials that fell below 500ms was 76.7% (SE = 2.8%, min =
1.1%, max = 99.6%), suggesting that for the majority of participants 500ms would prove a
suitable challenge for a response time threshold.
Pilot 2: SAT Manipulation Check
In this pilot study I introduced speed–accuracy manipulations by emphasising speed
and accuracy in separate blocks; this provided an opportunity to test whether the response
time threshold established in pilot study 1 was suitable. I also fitted the diffusion model to
the data.
Success of this pilot study would be determined by being able to successfully in-
duce speed–accuracy tradeoffs with our method (that is, by observing better accuracy in
“accuracy-emphasis” blocks, and shorter RTs in “speed-emphasis” blocks). Establishing
which diffusion model parameters are affected by speed–accuracy tradeoffs would also help
establish predictions for the main experiments.
Method.DEPRESSION & SPEED–ACCURACY TRADEOFFS 24
Observed Accuracy Observed Response Times
12
0.006
9
0.004
density
density
6
0.002
3
0 0.000
0.80 0.85 0.90 0.95 1.00 400 500 600 700
Mean Accuracy (Proportion) Mean Response Time (ms)
Figure 7 . Density distributions of the mean proportion accuracy and mean response times
from Pilot Study 1. ms = milliseconds.
Participants. 50 participants aged between 18–35 (inclusive) were recruited from
Prolific Academic residing in the UK or USA. The same exclusion criteria used in Pilot
Study 1 were used. Participants who took part in Pilot Study 1 were not eligible to take
part in this study.
Stimuli. The same stimuli from Pilot Study 1 were used.
Procedure. The task requirements were the same as in Pilot Study 1. However,
there were some general changes to the procedure in terms of instructions. I also added an
attention check (described below) to make sure participants were reading the instructions; I
removed participants who failed this attention check from the analysis.
When participants first entered the study, they were given general instructions that
initially did not mention speed–accuracy instructions. Instead, they were given general
instructions to learn the response requirements, and were then presented with a practice
block to learn the task. During this initial practice block, the instructions asked participants
to respond “as quickly and as accurately as possible”. Feedback was presented if participants
made an error in the same way as in Pilot Study 1. This initial practice consisted of 16
trials.
After this, participants were given instructions about the speed–accuracy emphasis
instructions. They were told that in the main study they would experience “accuracy-
emphasis” and “speed-emphasis” blocks. They were told that in accuracy-emphasis blocks,
they should respond “. . . as accurately as possible without taking too long to respond”. They
were told that in speed-emphasis blocks they should respond “. . . as quickly as possible
without making too many mistakes. DO NOT GUESS”. After these instructions, participants
were presented with a practice session where they were presented with 12 trials under
accuracy-emphasis instructions followed by 12 trials under speed-emphasis instructions.You can also read
